---

autotm/main.py

@@ -1,4 +1,6 @@
 import logging
+import os
+import pprint
 import tempfile
 from contextlib import contextmanager
 from typing import Optional
@@ -14,6 +16,38 @@
 logger = logging.getLogger()
 
 
+def obtain_autotm_params(
+        config_path: str,
+        topic_count: Optional[int],
+        lang: Optional[str],
+        alg: Optional[str],
+        surrogate_alg: Optional[str],
+        log_file: Optional[str]
+):
+    # TODO: define clearly format of config
+    if config_path is not None:
+        logger.info(f"Reading config from path: {os.path.abspath(config_path)}")
+        with open(config_path, "r") as f:
+            config = yaml.safe_load(f)
+    else:
+        config = dict()
+
+    if topic_count is not None:
+        config['topic_count'] = topic_count
+    if lang is not None:
+        pp = config.get('preprocessing_params', dict())
+        pp['lang'] = lang
+        config['preprocessing_params'] = pp
+    if alg is not None:
+        config['alg_name'] = alg
+    if surrogate_alg is not None:
+        config['surrogate_alg_name'] = surrogate_alg
+    if log_file is not None:
+        config['log_file_path'] = log_file
+
+    return config
+
+
 @contextmanager
 def prepare_working_dir(working_dir: Optional[str] = None):
     if working_dir is None:
@@ -29,9 +63,8 @@ def cli():
     pass
 
 
-# TODO: add lang and log_file params
 @cli.command()
-@click.option('--config', type=str, help="A path to config for fitting the model")
+@click.option('--config', 'config_path', type=str, help="A path to config for fitting the model")
 @click.option(
     '--working-dir',
     type=str,
@@ -48,56 +81,42 @@ def cli():
 )
 @click.option('--model', type=str, default='model.artm', help="A path that will contain fitted ARTM model")
 @click.option('-t', '--topic-count', type=int, help="Number of topics to fit model with")
+@click.option('--lang', type=str, help='Language of the dataset')
 @click.option('--alg', type=str, help="Hyperparameters tuning algorithm. Available: ga, bayes")
 @click.option('--surrogate-alg', type=str, help="Surrogate algorithm to use.")
+@click.option('--log-file', type=str, help="Log file path")
 def fit(
-        config: Optional[str],
+        config_path: Optional[str],
         working_dir: Optional[str],
         in_: str,
         out: str,
         model: str,
-        t: Optional[int],
+        topic_count: Optional[int],
+        lang: Optional[str],
         alg: Optional[str],
-        surrogate_alg: Optional[str]
+        surrogate_alg: Optional[str],
+        log_file: Optional[str]
 ):
-    # TODO: define clearly format of config
-    if config is not None:
-        with open(config, "r") as f:
-            config = yaml.load(f)
-    else:
-        config = dict()
+    config = obtain_autotm_params(config_path, topic_count, lang, alg, surrogate_alg, log_file)
 
-    cli_params = dict()
-    if t is not None:
-        cli_params['topic_count'] = t
-    if alg is not None:
-        cli_params['alg_name'] = alg
-    if surrogate_alg is not None:
-        cli_params['surrogate_alg_name'] = surrogate_alg
-
-    config = {
-        **config,
-        **cli_params
-    }
-
-    # TODO: log all params
+    logger.debug(f"Running AutoTM with params: {pprint.pformat(config, indent=4)}")
 
     df = pd.read_csv(in_)
 
     with prepare_working_dir(working_dir) as work_dir:
+        logger.info(f"Using working directory {os.path.abspath(work_dir)} for AutoTM")
         autotm = AutoTM(
             **config,
             working_dir_path=work_dir
         )
         mixtures = autotm.fit_predict(df)
 
-        logger.info(f"Calculated train mixtures: {mixtures.shape}\n\n{mixtures.head(10).to_string()}")
-
-        # saving artifacts
+        logger.info(f"Saving model to {os.path.abspath(model)}")
         autotm.save(model)
+        logger.info(f"Saving mixtures to {os.path.abspath(out)}")
         mixtures.to_csv(out)
 
-    click.echo('Initialized the database')
+    logger.info("Finished AutoTM")
 
 
 @cli.command()